Description:

Ext-RESCAL is a memory efficient implementation of RESCAL, a state-of-the-art algorithm for DEDICOM-like tensor factorization. Ext-RESCAL is written in Python and leverages the SciPy Sparse module.

• 3-D sparse tensor factorization [1]
• Joint 3-D sparse tensor and 2-D sparse matrix factorization (extended version) [2-3]
• Handy input format
• Support of float values
• The implementation provably scales well to the domains with millions of nodes on the affordable hardware.

[1] M. Nickel, V. Tresp, H. Kriegel. A Three-way Model for Collective Learning on Multi-relational Data // Proceedings of the 28th International Conference on Machine Learning (ICML'2011). - 2011.

[2] M. Nickel, V. Tresp, H. Kriegel. Factorizing YAGO: Scalable Machine Learning for Linked Data // Proceedings of the 21st international conference on World Wide Web (WWW'2012). - 2012.

[3] Nickel, Maximilian. Tensor factorization for relational learning. Diss. München, Ludwig-Maximilians-Universität, Diss., 2013, 2013.

Changes to previous version:

• Make the extended algorigthm output fixed (by replacing random initialization)
• Add handling of float values in the extended task
• Add the util for matrix pseudo inversion
• Switch to Apache License 2.0

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Csv

Tags: Tensor, Factorization

Archive: download here

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.